Early warning systems for financial distress: A machine learning approach to corporate risk mitigation

Abstract

This research explores the development of an early warning system for corporate financial distress using machine learning techniques to address key challenges in corporate risk mitigation. The main objective is to enhance predictive accuracy by integrating financial and non-financial data, addressing class imbalance, and ensuring model interpretability. The research design involves the formulation of a new machine learning model, leveraging cost-sensitive learning and feature selection, and is tested with a numerical example using logistic regression. Methodologically, the study adopts a data-driven approach that incorporates diverse financial ratios, macroeconomic variables, and market sentiment indicators to predict corporate distress. The numerical results from a basic logistic regression model demonstrate poor performance, especially in handling class imbalance, revealing limitations in traditional statistical models. However, the research suggests that machine learning methods, particularly ensemble learning with cost-sensitive algorithms, offer superior predictive accuracy and practical applicability. The study concludes that integrating advanced techniques and diverse datasets leads to more reliable early warning systems, with significant implications for corporate governance and financial risk management. Future research should explore more sophisticated machine learning models and extend real-world applications across various industries and economic conditions.

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